↓ Skip to main content

CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

Overview of attention for article published in Cellular and Molecular Bioengineering, October 2010
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 503)
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

blogs
1 blog
twitter
43 X users
patent
2 patents
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
54 Mendeley
citeulike
1 CiteULike
Title
CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models
Published in
Cellular and Molecular Bioengineering, October 2010
DOI 10.1007/s12195-010-0143-x
Pubmed ID
Authors

V. A. Shiva Ayyadurai, C. Forbes Dewey

Abstract

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models' source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated.

X Demographics

X Demographics

The data shown below were collected from the profiles of 43 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Austria 1 2%
Canada 1 2%
Unknown 50 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 30%
Student > Ph. D. Student 11 20%
Other 5 9%
Professor 4 7%
Student > Bachelor 4 7%
Other 6 11%
Unknown 8 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 31%
Biochemistry, Genetics and Molecular Biology 7 13%
Computer Science 6 11%
Engineering 5 9%
Medicine and Dentistry 3 6%
Other 6 11%
Unknown 10 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 46. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 March 2024.
All research outputs
#911,541
of 25,399,318 outputs
Outputs from Cellular and Molecular Bioengineering
#2
of 503 outputs
Outputs of similar age
#2,662
of 108,618 outputs
Outputs of similar age from Cellular and Molecular Bioengineering
#1
of 3 outputs
Altmetric has tracked 25,399,318 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 503 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 108,618 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them